Volumetric Analysis of Renal Masses as Predictors of Partial Nephrectomy Outcomes
Bibliographic record
Abstract
Objective: To examine the role of endophytic tumor volume (TV) assessment (endophycity) on perioperative partial nephrectomy (PN) outcomes. Patients and Methods: Retrospective review of 212 consecutive laparoscopic and open partial nephrectomies from single institution using preoperative imaging and 1-year follow-up. Demographics, comorbidities, RENAL nephrometry scores, and all peri- and postoperative outcomes were recorded. Volumetric analysis performed using imaging software, independently assessed by two blinded radiologists. Univariate and multivariate statistical analysis were completed to assess predictive value of endophycity for all clinically meaningful outcomes. Results: Among those undergoing minimally invasive surgery (MIS), lower tumor endophycity was associated with higher likelihood of trifecta outcome (negative surgical margin, <10% decline in estimated glomerular filtration rate, the absence of complications) irrespective of max tumor size. For MIS, estimated blood loss increased with greater tumor endophycity regardless of tumor size. Among those who underwent open partial nephrectomy, lower tumor endophycity was associated with trifecta outcomes for tumors >4 cm only. On multivariate analysis with log-scaled odds ratios (OR), tumor endophycity and total kidney volume had the strongest correlation with tumor-related complications (OR = 3.23, 2.66). The analysis identified that tumor endophycity and TV on imaging were inversely correlated with of trifecta outcomes (OR = 0.53 for both covariates). Conclusions: Volumetric assessment of tumor endophycity performed well in identifying PN outcomes. As automated imaging software improves, volumetric analysis may prove to be a useful adjunct in preoperative planning and patient counseling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".